Pro Spark Streaming


Автор: Zubair Nabi
Издательство: Apress

Год: 2016
Страниц: 231
Язык: английский
Формат: PDF
ISBN: 1484214803

 
Аннотация:

 Learn the right cutting-edge skills and knowledge to leverage Spark Streaming to implement a wide array of real-time, streaming applications. Pro Spark Streaming walks you through end-to-end real-time application development using real-world applications, data, and code. Taking an application-first approach, each chapter introduces use cases from a specific industry and uses publicly available datasets from that domain to unravel the intricacies of production-grade design and implementation. The domains covered in the book include social media, the sharing economy, finance, online advertising, telecommunication, and IoT.

In the last few years, Spark has become synonymous with big data processing. DStreams enhance the underlying Spark processing engine to support streaming analysis with a novel micro-batch processing model. Pro Spark Streaming by Zubair Nabi will enable you to become a specialist of latency sensitive applications by leveraging the key features of DStreams, micro-batch processing, and functional programming. To this end, the book includes ready-to-deploy examples and actual code. Pro Spark Streaming will act as the bible of Spark Streaming.

What You’ll Learn (Читая книгу, Вы освоите следующий материал):

  • Spark Streaming application development and best practices
  • Low-level details of discretized streams
  • The application and vitality of streaming analytics to a number of industries and domains
  • Optimization of production-grade deployments of Spark Streaming via configuration recipes and instrumentation using Graphite, collectd, and Nagios
  • Ingestion of data from disparate sources including MQTT, Flume, Kafka, Twitter, and a custom HTTP receiver
  • Integration and coupling with HBase, Cassandra, and Redis
  • Design patterns for side-effects and maintaining state across the Spark Streaming micro-batch model
  • Real-time and scalable ETL using data frames, SparkSQL, Hive, and SparkR
  • Streaming machine learning, predictive analytics, and recommendations
  • Meshing batch processing with stream processing via the Lambda architecture

 

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